Data-driven position estimation and collision detection for flexible manipulator

ABSTRACT

A flexible manipulator apparatus includes an elongate flexible manipulator having a sensor, a user output device configured to provide sensory outputs to the user, and processing circuitry. The flexible manipulator may be movable to form a curve in the flexible manipulator. The processing circuitry may be configured to receive captured sensor data from the sensor during movement of the flexible manipulator, and determine a collision likelihood score based on application of the captured sensor data to a collision detection model used for position estimation. The collision detection model may be based on an empirical data training for the flexible manipulator that includes training sensor data from the sensor and training image data of positions of the flexible manipulator. The processing circuitry may be configured to control the user output device based on the collision likelihood score to provide a collision alert sensory output to the user.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to and the benefit of prior-filed,co-pending U.S. Provisional Application No. 62/839,624 filed on Apr. 27,2019, the entire contents of which are hereby incorporated herein byreference.

STATEMENT OF GOVERNMENTAL INTEREST

This invention was made with Government support under contract numberR01EB016703-01A1 awarded by the National Institutes of Health (NIH). TheGovernment has certain rights in the invention.

TECHNICAL FIELD

Exemplary embodiments of the present disclosure generally relate toimplementation of flexible manipulators, and more specifically relate toimplementation of techniques for flexible manipulators to estimateposition and avoid environmental collisions.

BACKGROUND

Due to their flexibility, dexterity, and compact size, continuummanipulators (CMs) can enhance minimally invasive interventions,particularly in the medical arena. In these procedures, the CM may beoperated in proximity of sensitive organs and other tissue. Collisionsby the CM can damage such organs or tissue, and therefore avoidance ofsuch collisions can be of paramount concern when conducting invasiveprocedures. Conventional CM models, which may be used in associationwith collision avoidance, use complex characteristic information for thetype of CM device based on architectural characteristics, movementcharacteristics, and a priori information about the CM. Suchconventional models are complicated to generate and may not accuratelyrepresent the movement characteristics of a particular CM. As such, moresimplified and accurate approaches to generation of models used forcollision detection are needed that are not specific to an individualCM.

BRIEF SUMMARY OF SOME EXAMPLES

According to some example embodiments, a flexible manipulator apparatusis provided. The flexible manipulator apparatus may comprise an elongateflexible manipulator comprising a sensor. The flexible manipulator maybe movable to form a curve in the flexible manipulator. Further, theflexible manipulator apparatus may comprise a user output deviceconfigured to provide sensory outputs to the user, and the flexiblemanipulator apparatus may further comprise processing circuitry. Theprocessing circuitry may be configured to receive captured sensor datafrom the sensor during movement of the flexible manipulator, anddetermine a collision likelihood score based on application of thecaptured sensor data to a collision detection model used for positionestimation. In this regard, the collision detection model may be basedon an empirical data training for the flexible manipulator comprisingtraining sensor data from the sensor and training image data ofpositions of the flexible manipulator. The processing circuitry may alsobe configured to control the user output device based on the collisionlikelihood score to provide a collision alert sensory output to theuser.

According to some example embodiments, a system for generating acollision detection model for a flexible manipulator apparatus isprovided. The system may comprise an elongate flexible manipulatorcomprising a sensor. In this regard, the flexible manipulator may bemovable to form a curve in the flexible manipulator. The system mayfurther comprise a camera configured capture movement of the flexiblemanipulator relative to a test collision object, and processingcircuitry in communication with the flexible manipulator and the camera.The processing circuitry may be configured to control the flexiblemanipulator to cause iterations of movement relative to the testcollision object, with at least one iteration of movement involving acollision between the flexible manipulator and the test collisionobject. The processing circuitry may also be configured to receivesensor data sets associated with the iterations of movement of theflexible manipulator from the sensor, receive image data sets associatedwith the iterations of movement of the flexible manipulator from thecamera, synthesize the sensor data sets with the image data sets toclassify the iterations of movement into a collision class and a nocollision class, and generate the collision detection model for use withthe flexible manipulator for position estimation in future proceduresbased on the synthesized data sets and the classifications of theiterations of movement.

According to some example embodiments, a method for generating acollision detection model for a flexible manipulator apparatus isprovided. The method may comprise controlling a flexible manipulator tocause iterations of movement relative to a test collision object with atleast one iteration of movement involving a collision between theflexible manipulator and the test collision object, and receiving sensordata sets associated with the iterations of movement of the flexiblemanipulator from a sensor. In this regard, the flexible manipulator maycomprise the sensor. Further, the method may comprise receiving imagedata sets associated with the iterations of movement of the flexiblemanipulator from a camera. In this regard, the camera may be remote fromthe flexible manipulator. Further, the method may include synthesizing,by processing circuitry, the sensor data sets with the image data setsto classify the iterations of movement into a collision class and a nocollision class, and generating a collision detection model for use withthe flexible manipulator for position estimate in future proceduresbased on the synthesized data sets and the classifications of theiterations of movement.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

Having thus described some example embodiments of the invention ingeneral terms, reference will now be made to the accompanying drawings,which are not necessarily drawn to scale, and wherein:

FIG. 1 illustrates a flexible manipulator system for training andcollision detection via position estimation according to some exampleembodiments;

FIG. 2 illustrates a cross-section of a body of a flexible manipulatoraccording to some example embodiments;

FIG. 3 illustrates a perspective view of a portion of a sensor assemblyof a flexible manipulator according to some example embodiments;

FIG. 4 illustrates flow diagram for training and collision detection viaposition estimation according to an example embodiment;

FIG. 5 illustrates a collection of image views that may be used forclassifying a collision event and a no collision event according to someexample embodiments;

FIG. 6A illustrates an example architecture design of a deep neuralnetwork according to some example embodiments;

FIG. 6B illustrates an example hierarchical structure of a temporalneural network according to some example embodiments;

FIG. 7 illustrates a block diagram of an example method for generating acollision detection model for position estimation of a flexiblemanipulator apparatus according to some example embodiments; and

FIG. 8 illustrates a block diagram of an example method for determininga likelihood of a collision by a flexible manipulator apparatus usingposition estimation according to some example embodiments.

DETAILED DESCRIPTION

Some example embodiments now will be described more fully hereinafterwith reference to the accompanying drawings, in which some, but not allexample embodiments are shown. Indeed, the examples described andpictured herein should not be construed as being limiting as to thescope, applicability, or configuration of the present disclosure.Rather, these example embodiments are provided so that this disclosurewill satisfy applicable legal requirements. Like reference numeralsrefer to like elements throughout.

As used herein, the terms “component,” “module,” and the like areintended to include a computer-related entity, such as but not limitedto hardware, firmware, or a combination of hardware and software. Forexample, a component or module may be, but is not limited to being, aprocess running on a processor, a processor, an object, an executable, athread of execution, and/or a computer. By way of example, both anapplication running on a computing device and/or the computing devicecan be a component or module. One or more components or modules canreside within a process and/or thread of execution and acomponent/module may be localized on one computer and/or distributedbetween two or more computers. In addition, these components can executefrom various computer readable media having various data structuresstored thereon. The components may communicate by way of local and/orremote processes such as in accordance with a signal having one or moredata packets, such as data from one component/module interacting withanother component/module in a local system, distributed system, and/oracross a network such as the Internet with other systems by way of thesignal. Each respective component/module may perform one or morefunctions that will be described in greater detail herein. However, itshould be appreciated that although this example is described in termsof separate modules corresponding to various functions performed, someexamples may not necessarily utilize modular architectures foremployment of the respective different functions. Thus, for example,code may be shared between different modules, or the processingcircuitry itself may be configured to perform all of the functionsdescribed as being associated with the components/modules describedherein. Furthermore, in the context of this disclosure, the term“module” should not be understood as a nonce word to identify anygeneric means for performing functionalities of the respective modules.Instead, the term “module” should be understood to be a modularcomponent that is specifically configured in, or can be operably coupledto, the processing circuitry to modify the behavior and/or capability ofthe processing circuitry based on the hardware and/or software that isadded to or otherwise operably coupled to the processing circuitry toconfigure the processing circuitry accordingly.

Compared to conventional rigid-link robots, continuum manipulators (CMs)exhibit higher dexterity, flexibility, compliance, and conformity toconfined spaces, making them suitable for minimally invasiveinterventions. Examples of the use of CMs in medical applicationsinclude, but not limited to, neurosurgery, otolaryngology, cardiac,vascular, and abdominal interventions. In such medical applications, theCM may be used for steering in curvilinear pathways, manipulatingtissue, or merely as a flexible pathway for navigation of flexibleinstruments to a desired surgical site, all of which accentuating thenecessity of detecting CM collision or contact with bone, tissue, ororgans.

According to some example embodiments, a data-driven machine learningapproach is described herein using sensory information (and is someinstances only sensory information), without requiring any priorgeometrical assumptions, architectural or manufacturing model of the CM,or the surrounding environment. In this regard, according to someexample embodiments, a non-constant curvature CM, equipped with fiberBragg grating (FBG) optical sensors for shape sensing purposes may beused in association with a position estimation model to be used forcollision detection that is generated based on a data-driven, machinelearning approach to successfully detect collisions in constrainedenvironments with soft and hard obstacles with unknown stiffness andlocation.

According to some example embodiments, the data-driven generation of aposition estimation model for collision detection, as described herein,may be used to enhance safety during teleoperation or autonomous controlof CMs in proximity of sensitive organs in confined spaces. According tosome example embodiments, the generation of such a position estimationmodel may solely rely on data from on-device sensors, independent of theCM kinematics model, without any prior assumption or knowledge regardingthe geometry of the CM or its surrounding environment. In this regard,according to some example embodiments, generation of the positionestimation model for collision detection, which may be referred toherein as a collision detection model although the use of such asposition estimation model is not limited to use in the context ofcollision detection, may be conducted by defining the problem ofcollision detection as a classification problem, with sensoryinformation as the input, and occurrence/no-occurrence of CM collisionwith the environment as the output classes. A machine learning model maybe trained, for example preoperatively, on the sensory data from the CM,and then a generated model may be used to detect collisions, e.g.,intraoperatively, within an unknown environment. Feedback regarding thelikelihood of a collision, based on the generated collision detectionmodel, may be conveyed to the surgeon as audio or haptic feedback tosafeguard against potential trauma and damage to sensitive organs.

According to some example embodiments, different sensors, such aselectromagnetic tracking sensors, imaging sensors (e.g., intraoperative)or cameras may be used for sensing in CMs. However, according to someexample embodiments, FBG optical sensors may be used. FBG opticalsensors can offer advantages over other sensing approaches since suchsensors need not require a direct line of sight, may have high streamingrate, e.g., 1 kilohertz (KHz), and may have minimal effects oncompliance and compactness of the CM. FBG optical sensors may be used tosense and determine shape, force, and torsion of the CM. According tosome example embodiments, an FBG optical sensor may serve as adual-purpose simultaneous shape sensor and collision detector, which maypreserve small size of the CM and avoid the need for additional sensors.Accordingly, as further described herein, some example embodiments aredirected to a supervised machine learning approach for generating andutilizing a position estimation model for in CM collision detection withaudio feedback to the user, without requiring additional sensors, aprior model of the CM, obstacle location, or environment properties.

While the techniques described herein are described as be applicable toCMs, and more generally, flexible manipulators of devices, it iscontemplated that the training and collision detection may be applicablein a variety of applications, particularly, but not necessarily, in themedical context. For example, the techniques described herein may beimplemented in association with the position estimation or collisiondetection of other medical instruments such as core biopsy needles,catheters, and other flexible medical instruments.

In view of the foregoing, FIG. 1 illustrates and example system forgenerating or utilizing a collision detection model according to someexample embodiments. In this regard, the system 200 may comprise aflexible manipulator apparatus 100. The flexible manipulator apparatus100 may, in turn, comprise a control unit 110 and a flexible manipulator120. The flexible manipulator 120 may have a flexible elongate body 121that may comprise a tip 122. The body 121 may be repositioned intodifferent orientations along at least positions of the flexiblemanipulator 120's length to form curvatures. The flexible manipulator120 may be a continuum manipulator (CM) or a continuum dexterousmanipulator (CDM).

According to some example embodiments, a body 121 of the flexiblemanipulator 120 may be formed of a plastic substance or other flexiblematerial or structure. According to some example embodiments, the body121 may be may be formed of a “memory metal,” such as nitinol (NiTi),that reverts back to an original position after being bent. The body 121may include interleaved sections (or series of notches) that allow forbending and flexing of the body 121 while maintaining structuralintegrity. The body 121 may be formed as a tube that includes andinternal channel 123 suitable for passing flexible instruments throughthe internal channel 123. According to some example embodiments, theexternal diameter of the body 121 may be about 6 millimeters and theinternal diameter of the channel 125 may be about 4 millimeters.

The position and orientation of the body 121 of the flexible manipulator120 may be controlled via cables 132, which may be stainless steel orany other flexible cables. According to some example embodiments, thecables (or at least a body of the cables, as further described below)may be formed of stainless steel wire rope or any other flexible cableand may be, for example, 0.5 mm in diameter. The cables 132 may bedisposed in channels 125 within the body 121, where the channels 125extend along an elongated length of the body 121. In this regard, thebody 121 may include any number of channels 125, some of which may holda cable 132. The cables 132 may be movement controlled at the base viathe actuation unit 101, and the body 121 may also be affixed to theactuation unit 101. The operation of the actuation unit 101 isassociation with physical coupling with the body 121 and the cables 132may control the mechanical movement aspects of the flexible manipulator120. As such, through movement of the cables 132 responsive movement ofthe body 121 of the flexible manipulator 120 may be generated.

Additionally, the body 121 of the flexible manipulator 120 may includeat least one sensor 130 (e.g., and optical sensor) which may be operablycoupled to the sensor interface 104 of the control unit 110. In thisregard, the sensor 130 may be a FBG sensor or other imaging sensor.However, according to some example embodiments, the sensor 130 may be anelectromagnetic tracking sensor rather than an optical sensor. In thisregard, while example embodiments described herein may refer to opticalsensor data, it is understood that electromagnetic sensor data mayalternatively be used.

The sensor 130 may be comprised of one or more sensor assemblies 124.Such sensor assembly 124 may be formed as an elongate strand anddisposed in a respective channel 125 (FIG. 2) within the body 121,where, again, the channels 125 extend along an elongated length of thebody 121. A sensor assembly 124 may be operably coupled to the sensorinterface 104 to convert the signals generated by the sensor assembly124 into signals that can be used an interpreted by the processingcircuitry 106. According to some example embodiments, any number ofsensor assemblies 124 may be disposed in respective channels 125 of thebody 121. As further, described below, a sensor assembly 124 may itselfinclude sensor channels 129 (FIG. 3) within which a sensory component(e.g., in the form of an optical fiber 127 with FBG nodes 128) may bedisposed.

A sensor assembly 124 may be affixed at the tip 122 at a first end, aswell as, at the base (e.g., control unit 110) at a second end of theflexible manipulator 120. In this regard, at the second end, the sensorassembly 124 may be coupled to a sensor interface 104 of the controlunit 110.

With reference to FIG. 2, a cross-section of the body 121 of theflexible manipulator 120 taken a line A-A of FIG. 1 is shown. In thisregard, according to some example embodiments, the body 121 may have acircular cross-sectional shape. Further, the internal channel 123 (whichmay also be referred to as the instrument channel) of the body 121 mayalso have a circular cross-sectional shape. According to some exampleembodiments, the body 121 may include four channels 125, although,according to some example embodiments, any number of channels may beincluded. The channels 125 may extend along an elongate length of thebody 121 in a linear manner, or, according to some example embodiments,the channels 125 may be disposed in non-linear channels.

As shown in FIG. 2, cables 132 may be disposed in two of the channels125. Additionally, according to some example embodiments, a sensorassembly 124 may be disposed in each of two additional channels 125 ofthe body 121. Further, the sensor assemblies 124 may follow the path ofthe channels 125 through the body 121 as described above. Additionally,the sensor assemblies 124 may include sensor nodes that are alsopositioned in sensor assembly 124 and thus the path of the channels 125through the body 121. According to some example embodiments, rather thanthe sensor assemblies 124 being disposed within a channel 125, thesensor assemblies 124 may be disposed external to the body 121 of theflexible manipulator 120 and affixed to the external surface of the body121 along the elongate length of the flexible manipulator 120.

With respect to example embodiments of the sensor assemblies 124, FIG. 3illustrates a perspective view of a portion of a sensor assembly 124according to some example embodiments. As shown in FIG. 3, the sensorassembly 124 may be comprised of a sensor support structure 126 (orsensor substrate) which may be formed as a nitinol substrate. Further,the sensor support structure 126 may include one or more sensor channels129 disposed along an elongate length of the sensor support structure126. The sensor channels 129 may be configured to receive (and hold) asensory component of the sensor 130. According to some exampleembodiments, where the sensor 130 of the flexible manipulator 120 maycomprise one or more FBG sensors. In this regard, the sensor 130, as anFBG sensor, may comprise an optical fiber 127 with FBG nodes 128disposed along the optical fiber 127. Such FBG sensors may be relativelysmall in size and can provide data at high frequencies (e.g., 1 kHz)without requiring a direct line of sight (and are thus non-line-of-sightsensors). According to some example embodiments, the FBG sensors may becombined with a small elastic substrate to form standalone sensors andnot affixed to the cables 132 or the FBG sensors may be attacheddirectly to the cables 132 and may be disposed within a channel 125together. The optical fiber 127 with the FBG nodes 128 may be disposedin each of the sensor channels 129. The sensor channels 129 may extendalong an elongate length of the sensor support structure 126 in a linearmanner as shown in FIG. 3, or, according to some example embodiments,the sensor channels 129 may be non-linear. For example, the sensorchannels 129 may be formed in a helical path along the elongate lengthof the sensor support structure 126.

The FBG nodes 128 may be placed at any location along a length of thefiber 127. In operation, when the sensor assembly 124 bends, a strain isplaced on the FBG nodes 128 near the bend. As such, backscattered lightfrom the FBG nodes 128 that are under strain may have a differentwavelength, and therefore the wavelength of the backscattered light maybe analyzed to determine the degree of strain. When aggregated, theinformation provided by the backscattered light in the fiber 127 fromthe FBG nodes 128 (i.e., sensor data) can be used as a signature for anassociated position or curvature of the sensor assembly 124, and thusthe flexible manipulator 120 due to the positioning of the sensorassembly 124 in association with the body 121. Accordingly, whenconsidered in association with information or sensor data from othersensors of other sensor assemblies 124, for example, within a body 121of a flexible manipulator 120, a signature for a positioning of the body121 of the flexible manipulator 120 may also be determined and used formodeling.

The sensor support structure 126 of the sensor assembly 124 of an FBGsensor may be comprised of a substrate material (e.g., nitinol), and mayhave a geometric characteristic described by a number of fibers. Withrespect to geometry, a triangular configuration—fibers 127 disposed 120degrees apart (as shown in FIG. 3)—may be implemented. Alternatively, ahelically wrapped geometry may be used, for example, with three FBGfibers for simultaneous curvature/torsion/force measurements. In thisregard, according to some example embodiments, a helically wrappedsensor using only one fiber with multiple FBG nodes on the single fibermay be used.

Referring again to FIG. 1, the control unit 110 may be comprised of theactuation unit 101, a movement control interface 102, the sensorinterface 104, processing circuitry 106, and a sensory output device108. The actuation unit 101 may be configured to control the actuationand movement of the flexible manipulator 120. In this regard, theactuation unit 101 may include motors that are coupled to the cables132. Via rotation of the motors (e.g., direct current (DC) motors), thecables 132 may cause the body 121 of the flexible manipulator 120 tomove and bend into various desired positions. The motors may becontrolled by a user via the movement control interface 102. In thisregard, the movement control interface 102 may include a user interfacecontrol (e.g., a control pad, joystick, mouse, or the like) that ismoveable by a user to send signals to the motors to cause movement ofthe cables 132.

The sensor interface 104 may be configured, in some example embodiments,to perform an optical to electrical conversion of received optical orother signals. In this regard, the sensor interface 104 may beconfigured to input light onto, for example, the fiber 127 and receivebackscatter light from the fiber 127 that may be converted intoelectrical signals for delivery to the processing circuitry 106 foranalysis. In this regard, as further described below, the processingcircuitry 106 may be configured to analyze the sensor data representedby the electrical signal during a training process (e.g., preoperative)or during a collision detection process (e.g., intraoperative).Additionally, according to some example embodiments, during a collisiondetection process, the processing circuitry 106 may be configured tocontrol the sensory output device 108 to provide a feedback to the user(e.g., surgeon) regarding collision detection. In this regard, forexample, the sensory output device 108 may be a video device (e.g., adisplay), an audio device (e.g., a speaker), or a haptic feedback device(e.g., a controlled vibration device). According to some exampleembodiments where the sensory output device 108 is an audio device or ahaptic feedback device, the processing circuitry 106 may be configuredto control the sensory output device 108 to perform sonication based ona likelihood of a collision (e.g., a frequency or pitch of a tone outputby the sensory output device 108 may be increased or decreased as alikelihood of a collision increases).

Having described the flexible manipulator apparatus 100, the otherelements of the system 200 may now be described, with these elementsbeing components implemented during a training process, which could beperformed as a preoperative training process in some exampleembodiments. In this regard, the camera 210 may be a camera deviceconfigured to capture images and provide the images as image data foranalysis. According to some example embodiments, the camera 210 may be adigital camera device, an optical tracker, or the like. The camera 210may comprise one or a collection of cameras positioned to capture 2dimensional or 3 dimensional views of the flexible manipulator 120. Thecamera 210 may be directed at the flexible manipulator 120 (e.g., in anoverhead position) and may be configured to capture movements andpositions of the flexible manipulator 120 relative to a training object230, which may be placed in various locations during training togenerate a collision detection model. The camera 210 may provide thisimage data to a collision detection model generator 220. The collisiondetection model generator 220 may include processing circuitry 221configured to generate a collision detection model based on image datareceived from the camera 210 and sensor data from the sensor(s) of theflexible manipulator 120.

Having described the system 200, which may be implemented in the contextof a training, and the flexible manipulator apparatus 100, which may beimplemented in the context of a training process or in the context of acollision detection process, the methods and techniques for generatingand utilizing a collision detection model will be described. In thisregard, the collision detection model generator 220, and the processingcircuitry of the collision detection model generator 220, may beconfigured to generate a collision detection model by implementing atraining process as further described below.

With reference to flow diagram of FIG. 4, the training process 340 isdescribed and may involve receipt of image data sets from the camera 210of iterations of movements of the flexible manipulator 120, whichincludes the tip 122 and the sensor 130. The image data sets may beprovided to the collision detection model generator 220 to classify theimage data sets under image collision labeling 335. The classified imagedata sets may then be treated as training data 345. Additionally, duringthe iteration movements of the flexible manipulator 120, the sensor data325 (or FBG sensory data) may be captured from the sensor 130 and alsoprovided as training data 345. The training data 345 may be synthesizedand transformed into a machine learning model 350 as further describedbelow, which can be used for collision detection as a collisiondetection model.

Further, with respect to the collision detection process 355, which maybe implemented by the processing circuitry 106 of the flexiblemanipulator apparatus 100, the sensor data 325 may be received andapplied to the collision detection model by the collision detector 360.The collision detector 360 may provide outputs in the form of binaryoutputs (i.e., yes/collision at 375 or no/no collision at 380). Thecollision detector 360 may be employed to perform collision probabilityat 365 and apply the collision detection model in a probabilistic mannerto predict a likelihood of a collision in the form of a collisionlikelihood score (i.e., an analog or non-binary output) based on thesensor data being received, for example, over a given duration of time.Based on the collision likelihood score, sonification at 370 may beperformed via the sensory output device 108.

In this regard, providing additional detail with respect to thegeneration of a collision detection model, the problem of collisiondetection may be analyzed, according to some example embodiments, as asupervised classification problem with two classes: collision, and nocollision. Further, according to some example embodiments the classesmay be expanded to include classes directed to type, such as stiffnessproperties and regions of collision. In this regard, once a model isgenerated, the input may be the sensor data obtained from the sensor 130(e.g., fibers 127 with FBG nodes 128) and an output may be thecorresponding class of collision. To generate the model, the collisiondetection model generator 220 may be configured to create of an offlinedataset, where the sensor data, indicating a position of the flexiblemanipulator, may be labeled with a collision class based on interactionbetween the flexible manipulator 120 and the training object 230. Asupervised machine learning model 350 may be trained on the collecteddatasets to learn the nonlinear mapping from the sensor data 325 to theappropriate class of collision. The trained model may be furtheroptimized by tuning the hyperparameters via a k-fold cross validation bysplitting the training data to training and validation sets. Performanceof the tuned model may then be evaluated on unseen test data fromflexible manipulator collisions with obstacles with different stiffnessand properties (hard and soft), placed at random unknown locationsrelative to the flexible manipulator 120.

Additionally, according to some example embodiments, any number ofclasses may be defined for use in development of trained collisiondetection model. For example, classes may be defined for collision or nocollision of each lengthwise segment of the flexible manipulator 120.Segments of the manipulator may be defined as equal length portions formthe base to the tip or segments may be defined based on positions andlengths defined along the length of the flexible manipulator 120. Assuch, classes may be defined by the location or region of contact basedupon the segment that could be determined to make contact. For example,different classes collision/no collision classes could be defined forthe segments of the flexible manipulator such as the base, the center,and the tip. As such, the classes could distinguish between theparticular segment or segments that can come in contact with the objectby providing the likelihood that each segment may be contact withobjects.

To create an appropriate training dataset, a vision-based algorithmbased on a connected components labeling algorithm may be used tosegment training images captured via an overhead camera 210 looking atthe flexible manipulator 120 and the surrounding obstacles (e.g.,training object 230). Data (i.e., sensor data and image data) may becaptured for a number of movement iterations and varied positions of thetraining object 230 and synthesized for machine learning. According tosome example embodiments, the image data sets may first be converted toa binary format by applying appropriate thresholds (contrast or colorthresholds). Further, according to some example embodiments, an erosionmorphological operation, followed with a dilation, both with smallkernel sizes, may be applied to the binary image data to removepotential background noise and ensure robust connected region detection.The connected components labeling algorithm may then segment the binaryimage data to distinguish between the background, the flexiblemanipulator 120, and obstacles present in the scene. Particular imagedate and time-associated sensor data may be labeled with the collisionclass, if the flexible manipulator 120 and the obstacle form a connectedregion (i.e., a collision) in the corresponding image frame.

In this regard, FIG. 5 illustrates a collection of image views that maybe used for classifying a collision and a no collision, according tosome example embodiments. In this regard, the image views of FIG. 5indicate the segmented regions in collision and no collision instancesduring the training phase. An example “no collision” image data analysisis shown for a movement iteration in images 510, 520, and 530. In thisregard, the initial grayscale image 510 of the flexible manipulator 120and the training object 230 is converted into the binary image 520. Thebinary image 520 may then be analyzed to identify the background “1”,the training object “2”, and the flexible manipulator “3”. Since thereis no region of connectivity or continuity between the training object“2” and the flexible manipulator “3”, the image may be classified as ano collision image.

An example “collision” image data analysis is shown for a movementiteration in images 540, 550, and 560. In this regard, the initialgrayscale image 540 of the flexible manipulator 120 and the trainingobject 230 is converted into the binary image 550. The binary image 550may then be analyzed to identify the background “1” the training object“2”, and the flexible manipulator also “2”. Since there is connectivityor continuity between the training object and the flexible manipulator(both identified as region “2” in the image), the image may beclassified as a collision image.

The images and classifications may be incorporated into a data-drivenapproach to directly generate a collision detection model for flexiblemanipulator 120 based on empirical data captured during the trainingprocess. According to some example embodiments, because the collisiondetection problem may be addressed as a machine learning classificationproblem, a gradient boosting classifier may be implemented to learn andbuild the collision detection model. In this regard, gradient boostingmay be used as a powerful machine learning technique for classificationand regression problems. Gradient boosting may be implemented to build amodel in a sequential way and combines an ensemble of relatively weaksimple models (base learners). In this regard, let {x_(k)}_(k=1) ^(N) bea sequence of observations (image data sets), where x_(k)∈

^(n) (n is the number of the optical sensor data at frame k) representsthe observation at frame k. Let y_(k) be the corresponding labels(1=collision, 0=no collision). Given the training data, a gradientboosting classifier may be trained to detect a collision. The classifierscores may be used as a probability of predicting collisions based on astatistical analysis.

When generating the collision detection model, a number of parametersmay also be considered and optimized. In this regard, for the machinelearning on the training data, parameters such as learning rate,subsampling, number of max features, max depth, and boosting iterations(number of estimators) may be used. Further, regularization techniques,such as shrinkage, may be applied. According to some exampleembodiments, shrinkage may be applied to training data in combinationwith gradient boosting. Further, for example, parameters such asshrinkage (learning rate<1), stochastic gradient boosting (subsample<1),and variation on maximum number of features (using all or log 2 of thefeatures) may be applied to the machine learning. Further, according tosome example embodiments, a validation process may be performed usingk-fold cross validation on at least a portion of the training data.Additionally, according to some example embodiments, boosting trees,neural networks, deep neural networks, ensembles, and the like, may beused, according to some example embodiments, to perform sensing ofcontacts (collisions), shape, tip position, and force using, forexample, only sensor data as described herein.

Additionally, according to some example embodiments, the training datamay be analyzed to perform tip position estimation, which may also beused for shape sensing and contact force sensing in the context ofcollision detection. In this regard, according to some exampleembodiments, the position of the tip 122 may be estimated based on thedetermined curvature in the flexible manipulator 120 as indicated by thesensor data. Again, a data-driven or empirical approach may be used toestimate the tip location. To do so, according to some exampleembodiments, a regression model may be used, as a component of orintegrated into the collision detection model, that uses the sensor data(possibly in the form of FBG wavelength raw data) as an input anddirectly estimates tip position of the flexible manipulator 120. Again,the collision detection model may be pre-operatively (off-line) trainedon position information from cameras (e.g., camera 210) as the groundtruth, and then the collision detection model, with the tip estimationcontribution, may be applied intra-operatively (on-line) to detectpossible collisions based on estimates of the flexible manipulator tip.According to some example embodiments, the estimation of the tipposition may be performed using, for example, only the sensor data(e.g., FBG wavelength data). Such an approach may be beneficialparticularly when the flexible manipulator 120 is subjected to largedeflection bendings.

Similar to the techniques described above and as shown and describedwith respect to FIG. 4, tip position estimation may also be performed,using a tip position estimation model, without relying upon onassumptions regarding the geometrical relations of the manufacturedsensor and is therefore expected to reduce the error in tip positionestimation, especially when flexible manipulator 120 undergoes largedeflections. In addition, according to some example embodiments, adata-driven, empirical approach does not require any geometrical orstructural assumptions regarding the sensor design, the number of FBGnodes at each cross section of the flexible manipulator 120, but ratheruses the correlation and aggregation of, for example, all FBG nodes ofthe sensor along the elongate length of the flexible manipulator 120simultaneously to estimate the tip position. As the flexible manipulator120 bends, the sensor data in the form of raw FBG wavelength datachanges due to strain changes at the FBG nodes as described above.

For tip position estimation, as well as flexible manipulator 120 shapeestimation, a tip position estimation model (e.g., the collisiondetection model) may be trained on sensor data, such as FBG rawwavelength data as an input, and a true flexible manipulator tipposition (or shape) may be collected from another sensor (e.g., camera210) to synthesize the data into a relational model and classification.The model may be generated during the training process and thenleveraged to estimate the shape of the flexible manipulator 120 (i.e.,describing bends or curves in the flexible manipulator 120) and the tipposition intra-operatively using only the sensor data, in the form of,for example, the FBG raw wavelength data as an input.

For training, according to some example embodiments, a two-stepprocedure may be used to locate the tip position of the flexiblemanipulator 120 relative to the flexible manipulator 120's base usingthe camera 210, for example, in the form of an optical tracker, definingan optical tracker frame o. A coordinate frame may also be establishedat the tip 122, defined as frame s, when the tip 122 is straight withrespect to a reference body that is attached to the fixed actuation unit101, defining frame r, using, for example, a rigid body placed into theopen lumen (e.g., internal channel 123) of the flexible manipulator 120:T _(sr) ⁰=(T _(os) ⁰)⁻¹ T _(or) ⁰where T_(ij) ⁰ denotes frame j with respect to frame i during theone-time coordinate frame establishment ay time 0. According to someexample embodiments, using the transformation between frames s and r,all the information may be reported with respect to the reference body,independent of where the optical tracker is located.

For tip tracking during bending of the flexible manipulator 120, forexample, a single spherical marker attached to a post may be placed intothe internal channel 123 of the flexible manipulator 120. The trackedposition of the spherical marker with respect to the optical trackercoordinate (P_(o)) frame can be represented with respect to the base ofthe flexible manipulator 120, defined as frame b, by:P _(b) =T _(bs) T _(sr) ⁰(T _(or) ^(c))⁻¹ P _(o)where T_(or) ^(c) is the reference body coordinate frame with respect tothe optical tracker coordinate frame at the flexible manipulator 120'scurrent location (in case the location of optical tracker changes).T_(bs) brings the tip position in frame s to the flexible manipulator120 base frame (P_(b)):

$T_{bs} = \begin{bmatrix}I_{3 \times 3} & P_{bs} \\0_{1 \times 3} & 1\end{bmatrix}$where P_(bs)=[0, 0, L_(CDM)]^(T) and L_(CDM) is the length of theflexible manipulator 120. From the tracked position of the sphericalmarker with respect to the optical tracker coordinate (P_(o)) frame, thetip positions may be known with respect to the coordinate frame at thebase of the flexible manipulator 120 (frame b).

Based on the foregoing, a regression model may be used for generatingthe tip position estimation model (e.g., the collision detection model),since given some independent variables as input, prediction of adependent variable is desired. As such, a regression model may be usedthat can predict the flexible manipulator 120's three-dimensional tipposition (dependent variables) given a set of optical sensor data, forexample in the form of raw FBG wavelength data along the sensor 130(independent variables):p=Ψ(λ,β)where p∈

³ is the three-dimensional position of the tip 122, λ∈

^(m) is the vector containing the raw wavelength data of the m FBG nodeson the sensor, β is the vector of unknown parameters, and Ψ:

^(m)→

³ is the regression model that predicts the three-dimensional tipposition, given the wavelength information of the complete set of FBGnodes on the sensor 130 at any given time. According to some exampleembodiments, depending on the degrees of freedom of the flexiblemanipulator 120, complexity of the environment, and the shapes that theflexible manipulator 120 can obtain, different regression models such asboosting trees, neural networks, deep neural networks, ensembles, andthe like, may be used to capture the unknown parameters β.

For example, a linear regression may be used that models the dependentvariables as a linear combination of the unknown parameters.Accordingly, the tip position sensing may be modeled as a least squaresoptimization problem:

${\underset{B}{argmax}{\sum\limits_{n = 1}^{N}r_{n}^{2}}} = {\underset{B}{argmin}{{{\Lambda\; B} - P}}_{2}^{2}}$where r_(n) is the residual of the error for the n^(th) observation, Λ∈

^(N×m) is a stack of N observations of the m FBG node data, P∈

^(N×3) is the stack of N true tip position observation data (and Nmovement iterations of the flexible manipulator 120) from camera 210,and B∈

^(m×3) is the matrix of unknown parameters. Using the least squaresoptimization indicated above, the regression model may be trainedpre-operatively on N observations (movement iterations) of the FBGsensor data and the true tip position (from the camera 210) to find theunknown parameters β using the generalized inverse:B=(Λ^(T)Λ)⁻¹Λ^(T) P.

The trained model may then be used intra-operatively as the collisiondetection model to predict the tip position values given only thecurrent sensor data, in the form of FBG wavelength data:{circumflex over (p)}=B ^(T)λwhere {circumflex over (p)}∈

³ is the predicted tip position, given the current wavelength data forall FBG nodes on the sensor (λ∈

^(m)).

Using the regression model as provided above, the uncertaintiesregarding the shape sensor manufacturing such as FBG node locations canbe captured in the unknown parameters matrix B. In addition, thewavelength information of all FBG nodes (and not just the ones at acertain cross section) on the sensor may be utilized for tip positionprediction. Further, according to some example embodiments, theregression model does not suffer from the accumulated errors that occurduring integration of position over the length of the flexiblemanipulator 120 as provided in conventional methods, since the tipposition is predicted directly. Moreover, according to some exampleembodiments, the regression model to be used as the position estimationmodel or collision detection model can be trained separately on the datafrom fewer than three (or lesser) FBG fibers, to allow for the option ofpredicting the tip position in case of fiber failure in anintra-operative context by switching to use of the separately trainedmodel if a fiber failure is detected.

Alternatively, according to some example embodiments, a deep neuralnetwork (DNN) approach may be implemented to capture the unknownparameters. The DNN approach, according to some example embodiments, mayoffer increased flexibility to fit complex models compared to other,traditional regression methods. Unknown parameters in the DNN model maybe trained end-to-end in a data-driven manner through backpropagation.In this regard, the trainable parameters in the DNN may be defined asW={W^((i))}_(i=1) ^(L) for W^((i))∈

^(F) ^(i) ^(×F) ^(i-1) with a corresponding bias vector b^((i)) ∈

^(F) ^(i) , where i is the layer index and F_(i) is the layer sizehyperparameter. For sensor data in the form of a normalized raw FBGwavelength data vector at the n^(th) observation, {circumflex over(λ)}_(n)∈

^(m), activations may be computed as E={E_(n) ^((i))}_(i=1) ^(L) forE^((i))∈

^(F) ^(i) with:E _(n) ⁽¹⁾ =f(W ⁽¹⁾·{circumflex over (λ)}_(n) +b ⁽¹⁾).E _(n) ^((l)) =f(W ^((l)) ·E _(n) ^((l-1)) +b ^((l))),where f(⋅) is the Rectified Linear Unit (ReLU) non-linear activationfunction, l∈{2, . . . , L} are the second to the last layer of thenetwork and F₀=m. The DNN output may be the corresponding n^(th)flexible manipulator tip position {circumflex over (p)}_(n)∈

². A Mean Squared Error (MSE) may be used as a loss function to optimize∥{circumflex over (p)}_(n)−p_(n)|₂ ², where p_(n) ∈

² is the ground truth tip position observation. FIG. 6A illustrates anexample architecture design 570 of the DNN as described. In this regard,λ_(n) may be the raw FBG wavelength data vector at the n^(th)observation. {circumflex over (p)}_(n) may be the network outputflexible manipulator tip position with the hyperparameters of the fullyconnected layers being listed under each block in the architecturedesign 570.

Alternatively, according to some example embodiments, a temporal neuralnetwork (TNN) approach may be implemented to capture the unknownparameters. In this regard, the aforementioned models may be trained,according to some example embodiments, only with individual sensorobservations at a particular time step, without consideration of thetime-series information. Temporal Convolutional Networks (TCN) mayimprove video-based analysis models and make use of time-varyingfeatures to make predictions. Utilizing a time-series model may bebeneficial because input data features may change continuously, andmanipulation and deformation of the flexible manipulator 120 may alsooccur in a continuous manner. As such, inconsideration of the design ofTCNs, a TNN approach may be implemented for estimating the tip positionof the flexible manipulator 120.

In this regard, FIG. 6B illustrates a hierarchical structure 580 of aTNN, according to some example embodiments, with the concatenationprocess shown with the time-series data. Using the above notations, theinput concatenated feature with respect to time may be denoted as:{circumflex over (λ)}_(cat,n,k)=[{circumflex over (λ)}_(n−k), . . .,{circumflex over (λ)}_(n−1),{circumflex over (λ)}_(n),{circumflex over(λ)}_(n+1), . . . ,{circumflex over (λ)}_(n+k)]∈

^(m×(2k+1)),where (2k+1) is the number of samples covered in this concatenatedfeature. The TNN may be trained to predict the estimated tip positioncorresponding to the middle data sample {circumflex over (p)}_(n). Whileembedding the time-series may introduce a small delay (milliseconds) inpredictions, information about changes in the sensor data may beincorporated as a result of potential contacts with the environment orstopping the flexible manipulator 120 actuation. For the TNN, the sameMean Squared Error loss used in the DNN approach may be used.

Regardless of whether a linear regression, deep neural network, ortemporal neural network approach is used, once an accurate estimate ofthe tip position of the flexible manipulator 120 can be generated basedon the regression model, a shape of the flexible manipulator 120 can bereconstructed. In this regard, the flexible manipulator 120 may bemodeled as a series of rigid links connected by passive elastic jointsusing a pseudo-rigid body model. For example, the flexible manipulator120 may be modeled as an n-revolute-joint mechanism for a flexiblemanipulator 120 that is configured for planar motion. Further, dependingon the design of the particular flexible manipulator 120, sphericaljoints may be assumed in a general case. As such, at any given instance,the tip position estimation ({circumflex over (p)}) output from theregression model may be passed as an input to a constrained optimizationas follows to solve for the joint angles, and consequently, the shapemay be reconstructed:

$\underset{\Theta_{c}}{minimize}{{\hat{p} - {f\left( \Theta_{c} \right)}}}$subject  to  Θ_(c) ≤ Θ_(ma x)$f_{x} = {d \cdot \left( {\sum\limits_{i = 1}^{n}{\sin\left( {\sum\limits_{j = 1}^{i}\Theta_{j}} \right)}} \right)}$$f_{y} = {d \cdot \left( {\sum\limits_{i = 1}^{n}{\cos\left( {\sum\limits_{j = 1}^{i}\Theta_{j}} \right)}} \right)}$where Θ_(c)∈

^(n) may be the flexible manipulator 120 joint angles from thepseudo-rigid body model, d=L_(c)/n is the distance between twoconsecutive joints, f(Θ_(c)): Θ_(c)→

² is the flexible manipulator 120 forward kinematics mapping from jointspace to task space, and Θ_(max) is the maximum angle each joint maytake, which can be determined experimentally. A trade-off may existbetween using a more complex pseudo-rigid body model (large number forn) and the computational complexity of the constrained optimizationabove. Additionally, with respect to outputs provided to the user (e.g.,via the sensory output device) during a collision detection process,visual feedback may be used and is common means of conveying informationto, for example, the surgeon during surgery. However, augmentation ofthis feedback method with many sources of information may be prone tothe risk of missing crucial information in the visual context.Particularly, during teleoperation and control of the flexiblemanipulator 120 in confined spaces, the visual feedback may becompromised by occlusion or miss-interpretation due to clutteredbackground, which may increase the risk of damaging sensitive organs ortissues. As an alternative, information may be conveyed to the surgeon(user) associated with the collision detection via sonification of theprobability of predicting collision (classifier or collision likelihoodscore).

Having described the various features and techniques associated withgenerating and utilizing a collision detection model, specificconfigurations of the processing circuitry 106 of the control unit 110and the processing circuitry 221 of the collision detection modelgenerator 220 can be described, referring again to FIG. 1. Processingcircuitry 106, 221 may comprise a processor, and a memory, in additionto other passive and active components for performing thefunctionalities described herein. Further, according to some exampleembodiments, processing circuitry 106, 221 may be in operativecommunication with or embody, the memory, the processor, and possibly auser interface, and a communications interface. Through configurationand operation, the processing circuitry 106, 221 may be configurable toperform various operations as described herein, including the operationsand functionalities described with respect to the functionality of theflexible manipulator apparatus 100 or the collision detection modelgenerator 220. In this regard, the processing circuitry 106, 221 may beconfigured to perform computational processing, memory management, userinterface control, and monitoring, and manage remote communications,according to an example embodiment. In some embodiments, the processingcircuitry 106, 221 may be embodied as a chip or chip set. In otherwords, the processing circuitry 106, 221 may comprise one or morephysical packages (e.g., chips) including materials, components or wireson a structural assembly (e.g., a baseboard). The processing circuitry106, 221 may be configured to receive inputs (e.g., via peripheralcomponents), perform actions based on the inputs, and generate outputs(e.g., for provision to peripheral components). In an exampleembodiment, the processing circuitry 106, 221 may include one or moreinstances of a processor, associated circuitry, and memory. As such, theprocessing circuitry 106, 221 may be embodied as a circuit chip (e.g.,an integrated circuit chip, such as a field programmable gate array(FPGA)) configured (e.g., with hardware, software or a combination ofhardware and software) to perform operations described herein.

In an example embodiment, the memory may include one or morenon-transitory memory devices such as, for example, volatile ornon-volatile memory that may be either fixed or removable. The memorymay be configured to store information, data, applications, instructionsor the like for enabling, for example, the functionalities describedwith respect to the flexible manipulator apparatus 100 or the collisiondetection model generator 220. The memory may operate to bufferinstructions and data during operation of the processing circuitry 106,221 to support higher-level functionalities, and may also be configuredto store instructions for execution by the processing circuitry 106,221. According to some example embodiments, various data stored in thememory may be generated based on other data and stored or the data maybe retrieved via a communications interface linked to another entity andstored in the memory.

As mentioned above, the processing circuitry 106, 221 may be embodied ina number of different ways. For example, the processing circuitry 106,221 may be embodied as various processing means such as one or moreprocessors that may be in the form of a microprocessor or otherprocessing element, a coprocessor, a controller or various othercomputing or processing devices including integrated circuits such as,for example, an ASIC (application specific integrated circuit), an FPGA,or the like. In an example embodiment, the processing circuitry may beconfigured to execute instructions stored in the memory or otherwiseaccessible to the processing circuitry 106, 221. As such, whetherconfigured by hardware or by a combination of hardware and software, theprocessing circuitry 106, 221 may represent an entity (e.g., physicallyembodied in circuitry—in the form of processing circuitry 106, 221)capable of performing operations according to example embodiments whileconfigured accordingly. Thus, for example, when the processing circuitry106,221 is embodied as an ASIC, FPGA, or the like, the processingcircuitry 106, 221 may be specifically configured hardware forconducting the operations described herein. Alternatively, as anotherexample, when the processing circuitry 106, 221 is embodied as anexecutor of software instructions, the instructions may specificallyconfigure the processing circuitry 106, 221 to perform the operationsdescribed herein.

With respect to the configuration of the processing circuitry 221 of thecollision detection model generator 220, in general, the processingcircuitry 221 may be configured to perform a training process togenerate a collision detection model for use in later collisiondetection. In this regard, the processing circuitry 221 may becommunication with the camera 210 and the flexible manipulator apparatus100. As described above, the flexible manipulator apparatus 100 maycomprise a flexible manipulator 120 that may be elongate and maycomprise a sensor (e.g., sensor 130). In this regard, the sensor may bean FBG sensor. Further, according to some example embodiments, thesensor may comprise three sensor nodes, where each of the three sensornodes is located on a different, respective cross-sectional plane of theflexible manipulator 120, and each sensor node is configured to collectnode-level sensor data for inclusion in the sensor data sets. Accordingto some example embodiments, the sensor may be configured to provide rawsensor data that includes wavelength information. The flexiblemanipulator 120 may be movable to form a curve in the flexiblemanipulator 120. The flexible manipulator 120 may be a continuummanipulator controlled via movement of cables 132. The flexiblemanipulator apparatus 100 may also include a user output device (e.g.,sensory output device 108), which may be configured to provide sensoryoutputs to a user. The camera 210 may be configured to capture movementof the flexible manipulator 120 relative to a test collision object(e.g., training object 230).

Accordingly, the processing circuitry 221 may be configured to controlthe flexible manipulator 120, via the processing circuitry 106, causeiterations of movement of the flexible manipulator 120 relative to thetest collision object with at least one iteration of movement involvinga collision between the flexible manipulator 120 and the test collisionobject. The processing circuitry 221 may be further configured toreceive sensor data sets (e.g., from the sensor 130) associated with theiterations of movement of the flexible manipulator 120 (e.g., associatedin time). The processing circuitry 221 may also be configured to receiveimage data sets, from the camera 210, associated with the iterations ofmovement of the flexible manipulator 120.

Having received the sensor data sets and the image data sets, theprocessing circuitry 221 may be configured to synthesize the sensor datasets with the image data sets to classify the iterations of movementinto a collision class and a no collision class. Further, the processingcircuitry 221 may be configured to generate a collision detection modelfor use with the flexible manipulator 120 in future procedures based onthe synthesized data sets and the classifications of the iterations ofmovement. In this regard, as described above, the collision detectionmodel may be generated based on machine learning and/or a regressionmodel. According to some example embodiments, the processing circuitry221 may be configured to generate the collision detection model throughimplementation of a gradient boosting classifier. Further, according tosome example embodiments, the processing circuitry 221 may be configuredto generate the collision detection model via k-fold cross validation onthe synthesized data sets.

The processing circuitry 106 may be generally configured to detectionpotential collisions of the flexible manipulator 120 during a collisiondetection procedure. In the regard, the processing circuitry 106 may beconfigured to receive captured sensor data from the sensor (e.g., sensor130) during movement of the flexible manipulator. The processingcircuitry 106 may also be configured to determine a collision likelihoodscore based on application of the captured sensor data to a collisiondetection model (e.g., generated by the collision detection modelgenerator 220). In this regard, the collision detection model may bebased on an empirical data (or data-driven) training for the flexiblemanipulator 120 comprising training sensor data from the sensor andtraining image data of positions of the flexible manipulator 120. Theprocessing circuitry 106 may also be configured to control the useroutput device (e.g., sensory output device 108) based on the collisionlikelihood score to provide a collision alert sensory output to theuser. In this regard, according to some example embodiments, theprocessing circuitry 106 may be configured to control the user outputdevice to provide a real-time sonication output to the user based on thecollision likelihood score. Further, according to some exampleembodiments, the processing circuitry 106 may be configured to determinethe collision likelihood score based on application of the capturedsensor data to the collision detection model, where the captured sensordata is the only input to be applied to the collision detection model todetermine the collision likelihood score and/or the captured sensor datais raw sensory data received from the sensor. Further, according to someexample embodiments, the processing circuitry 106 may be configured todetermine an estimation of a position of the tip by determining acurvature of the flexible manipulator based on the captured sensor data.

Now referring to FIG. 7, an example method for generating a collisiondetection model for a flexible manipulator apparatus (e.g., flexiblemanipulator apparatus 100) is provided. In this regard, the examplemethod may comprise, at 600, controlling a flexible manipulator to causeiterations of movement relative to a test collision object with at leastone iteration of movement involving a collision between the flexiblemanipulator and the test collision object. The example method may alsocomprise, at 610, receiving sensor data sets associated with theiterations of movement of the flexible manipulator from a sensor. Inthis regard, the flexible manipulator may comprise the sensor. Further,the example method may also comprise, at 620, receiving image data setsassociated with the iterations of movement of the flexible manipulatorfrom a camera. In this regard, the camera may be remote from theflexible manipulator. Further, the example method, at 630, may includesynthesizing, by processing circuitry, the sensor data sets with theimage data sets to classify the iterations of movement into a collisionclass and a no collision class, and, at 640, generating a collisiondetection model for use with the flexible manipulator in futureprocedures based on the synthesized data sets and the classifications ofthe iterations of movement.

According to some example embodiments, generating the collisiondetection model may include generating the collision detection modelthrough implementation of a gradient boosting classifier. Further,according to some example embodiments, the sensor may include an FBGsensor. Further, according to some example embodiments, generating thecollision detection model may include generating the collision detectionmodel via k-fold cross validation on the synthesized data sets.Additionally, according to some example embodiments, the sensor maycomprise three sensor nodes. Each of the three sensor nodes may belocated on a different, respective cross-sectional plane of the flexiblemanipulator. Further, each sensor node may be configured to collectnode-level sensor data for inclusion in the sensor data sets.

Now referring to FIG. 8, an example method for determining a likelihoodof a collision by a flexible manipulator apparatus (e.g., flexiblemanipulator apparatus 100) is provided. In this regard, the examplemethod may include, at 700, receiving captured sensor data from thesensor during movement of the flexible manipulator. Further, at 710, theexample method may include determining a collision likelihood scorebased on application of the captured sensor data to a collisiondetection model. In this regard, the collision detection model may bebased on an empirical data training for the flexible manipulatorcomprising training sensor data from the sensor and training image dataof positions of the flexible manipulator. Additionally, the examplemethod may comprise controlling the user output device based on thecollision likelihood score to provide a collision alert sensory outputto the user.

According to some example embodiments, controlling the user outputdevice may include controlling the user output device to provide areal-time sonication output to the user based on the collisionlikelihood score. According to some example embodiments, the flexiblemanipulator may comprise a continuum manipulator controlled via movementof cables, and/or the sensor may include an FBG sensor. Further,according to some example embodiments, receiving the captured sensordata may include receiving the captured sensor data as raw sensor datathat includes wavelength information. According to some exampleembodiments, determining the collision likelihood score may includedetermining the collision likelihood score based on application of thecaptured sensor data to the collision detection model. In this regard,the captured sensor data may be the only input to be applied to thecollision detection model to determine the collision likelihood score.Alternatively, according to some example embodiments, determining thecollision likelihood score may include determining the collisionlikelihood score based on application of the captured sensor data to thecollision detection model. In this regard, the captured sensor data maybe raw sensory data received from the sensor. Further, according to someexample embodiments, determining the collision likelihood score mayinclude determining an estimation of a position of the tip bydetermining a curvature of the flexible manipulator based on thecaptured sensor data. According to some example embodiments, the sensormay comprise three sensor nodes, where each of the three sensor nodesbeing located on a different, respective cross-sectional plane of theflexible manipulator. In this regard, each sensor node may be configuredto collect node-level sensor data for inclusion in the sensor data.

Many modifications and other embodiments of the inventions set forthherein will come to mind to one skilled in the art to which theseinventions pertain having the benefit of the teachings presented in theforegoing descriptions and the associated drawings. Therefore, it is tobe understood that the inventions are not to be limited to the specificembodiments disclosed and that modifications and other embodiments areintended to be included within the scope of the appended claims.Moreover, although the foregoing descriptions and the associateddrawings describe exemplary embodiments in the context of certainexemplary combinations of elements and/or functions, it should beappreciated that different combinations of elements and/or functions maybe provided by alternative embodiments without departing from the scopeof the appended claims. In this regard, for example, differentcombinations of elements and/or functions than those explicitlydescribed above are also contemplated as may be set forth in some of theappended claims. In cases where advantages, benefits or solutions toproblems are described herein, it should be appreciated that suchadvantages, benefits and/or solutions may be applicable to some exampleembodiments, but not necessarily all example embodiments. Thus, anyadvantages, benefits, or solutions described herein should not bethought of as being critical, required, or essential to all embodimentsor to that which is claimed herein. Although specific terms are employedherein, they are used in a generic and descriptive sense only and notfor purposes of limitation.

What is claimed is:
 1. A manipulator apparatus comprising: a flexiblemanipulator comprising a sensor, wherein the flexible manipulator ismovable to form a curve; a user output device configured to providesensory outputs to a user; and processing circuitry configured to:receive captured sensor data from the sensor during movement of theflexible manipulator; determine a collision likelihood score based onapplication of the captured sensor data to a collision detection modelused for position estimation, the collision detection model being basedon an empirical data training for the flexible manipulator comprisingtraining sensor data from the sensor and training image data ofpositions of the flexible manipulator; and control the user outputdevice based on the collision likelihood score to provide a collisionalert sensory output to the user.
 2. The manipulator apparatus of claim1, wherein the processing circuitry is configured to control the useroutput device to provide a real-time sonification output to the userbased on the collision likelihood score.
 3. The manipulator apparatus ofclaim 1, wherein the flexible manipulator comprises a continuummanipulator controlled via movement of cables.
 4. The manipulatorapparatus of claim 1, wherein the sensor comprises a fiber Bragg grating(FBG) sensor.
 5. The manipulator apparatus of claim 1, wherein thesensor is configured to provide raw sensor data that includes wavelengthinformation.
 6. The manipulator apparatus of claim 1 wherein theprocessing circuitry is configured to determine the collision likelihoodscore based on application of the captured sensor data to the collisiondetection model, and the captured sensor data is the only input to beapplied to the collision detection model to determine the collisionlikelihood score.
 7. The manipulator apparatus of claim 1 wherein theprocessing circuitry is configured to determine the collision likelihoodscore based on application of the captured sensor data to the collisiondetection model, and the captured sensor data is raw sensory datareceived from the sensor.
 8. The manipulator apparatus of claim 1,wherein the flexible manipulator comprises a tip, and the processingcircuitry configured to determine the collision likelihood scoreincludes being configured to determine an estimation of a position ofthe tip by determining a curvature of the flexible manipulator based onthe captured sensor data.
 9. The manipulator apparatus of claim 1,wherein the sensor comprises three or more sensor nodes, each of thesensor nodes is located on a different, respective cross-sectional planeof the flexible manipulator, and each sensor node is configured tocollect node-level sensor data for inclusion in the sensor data.
 10. Asystem for generating a collision detection model for a manipulatorapparatus, the system comprising: a flexible manipulator comprising ansensor, wherein the flexible manipulator is movable to form a curve; acamera configured capture movement of the flexible manipulator relativeto a test collision object; processing circuitry in communication withthe flexible manipulator and the camera, the processing circuitryconfigured to: control the flexible manipulator to cause iterations ofmovement relative to the test collision object with at least oneiteration of movement involving a collision between the flexiblemanipulator and the test collision object; receive sensor data setsassociated with the iterations of movement of the flexible manipulatorfrom the sensor; receive image data sets associated with the iterationsof movement of the flexible manipulator from the camera; synthesize thesensor data sets with the image data sets to classify the iterations ofmovement into a collision class and a no collision class; and generatethe collision detection model for use with the flexible manipulator forposition estimation in future procedures based on the synthesized datasets and the classifications of the iterations of movement.
 11. Thesystem of claim 10, wherein the processing circuitry is configured togenerate the collision detection model through implementation of machinelearning using a deep neural network, a gradient boosting classifier, ora linear regression model.
 12. The system of claim 10, wherein theflexible manipulator comprises a continuum manipulator controlled viamovement of cables.
 13. The system of claim 10, wherein the sensorcomprises a fiber Bragg grating (FBG) sensor.
 14. The system of claim10, wherein the processing circuitry is configured to validate thecollision detection model using the synthesized data sets.
 15. Thesystem of claim 10, wherein the sensor comprises three or more sensornodes, each of the sensor nodes is located on a different, respectivecross-sectional plane of the flexible manipulator, and each sensor nodeis configured to collect node-level sensor data for inclusion in thesensor data sets.
 16. A method for generating a collision detectionmodel for a manipulator apparatus, the method comprising: controlling aflexible manipulator to cause iterations of movement relative to a testcollision object with at least one iteration of movement involving acollision between the flexible manipulator and the test collisionobject; receiving sensor data sets associated with the iterations ofmovement of the flexible manipulator from a sensor, wherein the flexiblemanipulator comprises the sensor; receiving image data sets associatedwith the iterations of movement of the flexible manipulator from acamera, the camera being remote from the flexible manipulator;synthesizing, by processing circuitry, the sensor data sets with theimage data sets to classify the iterations of movement into a collisionclass and a no collision class; and generating a collision detectionmodel for use with the flexible manipulator for position estimation infuture procedures based on the synthesized data sets and theclassifications of the iterations of movement.
 17. The method of claim16, wherein the generating the collision detection model includesimplementation of a classifier such as gradient boosting classifier. 18.The method of claim 16, wherein the sensor comprises a fiber Bragggrating (FBG) sensor.
 19. The method of claim 16, wherein the generatingthe collision detection model includes validating the collisiondetection model using the synthesized data sets.
 20. The method of claim16, wherein the sensor comprises three or more sensor nodes, each of thesensor nodes being located on a different, respective cross-sectionalplane of the flexible manipulator, and each sensor node being configuredto collect node-level sensor data for inclusion in the sensor data sets.